Regularization Paths of L1-Penalized SVMs

Computes regularization paths for L1-penalized support vector machine (SVM) models, including the standard SVM, receiver operating characteristic SVM (ROC-SVM), and fraud detection SVM (FD-SVM). The package implements efficient path algorithms to obtain solutions over a wide range of regularization parameter values. It also provides cross-validation for selecting an optimal regularization parameter, along with functions for prediction, visualization, and summarization of the fitted models. For more details, see Zhu et al. (2003) < https://proceedings.neurips.cc/paper_files/paper/2003/file/49d4b2faeb4b7b9e745775793141e2b2-Paper.pdf>, Kim et al. (2021) , Park et al. (2023) .